Embodiments of the disclosure provide an image rendering method, an image rendering apparatus, an image processing device, and a computer-readable storage medium. In one embodiment, the method, apparatus, device, and medium involve obtaining an initial image of a current scene, and determining a first area and a second area on the initial image; rendering image data of the first area based on a first rendering rule, to obtain a first sub-image; rendering image data of the second area based on a second rendering rule, to obtain a second sub-image; and generating a target display image based on the first sub-image and the second sub-image, the first rendering rule being different from the second rendering rule.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. An image rendering method, performed by an image processing device, the method comprising: obtaining an initial image of a current scene, and determining a first area and a second area on the initial image; rendering first image data of the first area based on a first rendering rule, to obtain a first sub-image; rendering second image data of the second area based on a second rendering rule, to obtain a second sub-image; and generating a target display image based on the first sub-image and the second sub-image, the first rendering rule being different from the second rendering rule, wherein the rendering the first image data of the first area comprises: obtaining color image data of the first area from the initial image, and obtaining depth image data of the first area, the depth image data indicating information about a distance between a scene object in the initial image and an eye of a user; and performing visual depth-of-field rendering on the color image data such that a color value of a target pixel in non-gaze point pixels is adjusted based on a difference between a reference focal length of the target pixel in the non-gaze point pixels and a target focal length of a gaze point pixel.
This invention relates to image rendering techniques for enhancing visual depth perception in displayed images. The method addresses the problem of creating more realistic and immersive visual experiences by dynamically adjusting image rendering based on user gaze and depth information. The system obtains an initial image of a scene and identifies two distinct areas within it. The first area is processed using a first rendering rule that applies depth-of-field effects, while the second area is processed using a different rendering rule. For the first area, color image data and depth image data are extracted. The depth data indicates the distance between scene objects and the user's eye. The color values of pixels outside the user's gaze point are adjusted based on the difference between a reference focal length (for non-gaze pixels) and a target focal length (for the gaze point pixel). This creates a natural depth-of-field effect, where objects in focus appear sharper while out-of-focus areas are blurred proportionally to their distance from the focal point. The processed sub-images from both areas are then combined to generate the final display image. This approach improves visual realism by dynamically adapting rendering based on gaze and depth, enhancing user immersion in virtual or augmented reality applications.
2. The method according to claim 1 , wherein the determining the first area and the second area comprises: performing tracking of the eye of the user, to determine a gaze point on the initial image; determining the first area based on the gaze point and a target field of view (FOV); and determining the second area based on the initial image and the first area.
This invention relates to eye-tracking technology for enhancing image processing, particularly in systems where a user's gaze is used to dynamically adjust image regions of interest. The problem addressed is the need to efficiently prioritize and process different areas of an image based on where a user is looking, optimizing computational resources and improving user experience. The method involves tracking a user's eye to determine a gaze point on an initial image. Based on this gaze point and a predefined target field of view (FOV), a first area of the image is identified as the primary region of interest. This first area is where the user is actively looking, and it may require higher resolution or more detailed processing. The second area is then determined based on the initial image and the first area, likely encompassing the remaining portions of the image that may receive less processing priority. The method ensures that computational resources are allocated efficiently, focusing on the most relevant parts of the image while maintaining acceptable quality for the rest. This approach is useful in applications such as virtual reality, augmented reality, or any system where real-time image processing is influenced by user attention.
3. The method according to claim 1 , wherein the performing the visual depth-of-field rendering comprises: determining the gaze point pixel from the color image data; determining depth information corresponding to the gaze point pixel based on the depth image data, and determining the target focal length of the user based on the determined depth information; and determining the reference focal length of the target pixel in the non-gaze point pixels in the color image data based on the target focal length and the depth image data.
This invention relates to visual depth-of-field rendering in imaging systems, specifically for enhancing focus based on user gaze. The problem addressed is the lack of dynamic focus adjustment in conventional imaging systems, which often produce images with fixed focal lengths that do not adapt to the user's visual attention. The method involves analyzing a color image and corresponding depth image to determine a gaze point pixel, which represents the area of interest for the user. Depth information for this pixel is extracted from the depth image data, and a target focal length is calculated based on this depth. The system then determines a reference focal length for other pixels in the image, adjusting their focus relative to the target focal length and the depth data. This creates a depth-of-field effect that prioritizes the gaze point while maintaining contextual focus for surrounding areas. The technique improves image clarity and user experience by dynamically adapting focus to the user's visual focus, simulating natural human vision. The method is particularly useful in applications like augmented reality, virtual reality, and advanced camera systems where dynamic focus enhancement is desired.
4. The method according to claim 3 , wherein the performing the visual depth-of-field rendering further comprises: determining differential information between a reference focal length of each target pixel in the non-gaze point pixels and the target focal length; determining a mapping value of each target pixel according to the differential information, and selecting, from a reference layer set, a target layer based on the mapping value of each target pixel; and determining a color value of each target pixel according to a color value of a pixel that has a same image position as each target pixel on the target layer.
This invention relates to visual depth-of-field rendering in imaging systems, particularly for enhancing image clarity in regions outside a user's gaze point. The problem addressed is the limited ability of conventional systems to dynamically adjust focus and depth effects based on gaze tracking, resulting in blurred or distorted non-gaze regions. The solution involves a method for rendering depth-of-field effects by analyzing differential information between a reference focal length of each pixel in non-gaze regions and a target focal length. A mapping value is calculated for each pixel based on this differential information, which is then used to select a target layer from a pre-existing reference layer set. The color value of each pixel is determined by referencing the corresponding pixel position on the selected target layer, ensuring accurate depth-of-field rendering. The reference layer set contains multiple layers with varying depth information, allowing for precise adjustments. This approach improves image quality by dynamically adapting focus effects to non-gaze regions while maintaining computational efficiency. The method is particularly useful in applications like virtual reality, augmented reality, and advanced imaging systems where gaze tracking is integrated with visual rendering.
5. The method according to claim 4 , wherein the reference layer set comprises a plurality of reference layers, the plurality of reference layers have a same image size, and resolutions of the plurality of reference layers are different and less than an image resolution corresponding to the color image data.
This invention relates to image processing, specifically methods for generating and using reference layers to enhance image analysis or reconstruction. The problem addressed is the computational and memory inefficiency of processing high-resolution images directly, particularly when multiple reference layers are needed for tasks like image segmentation, object detection, or super-resolution reconstruction. The method involves creating a set of reference layers derived from input color image data. Each reference layer in the set has the same image size as the original but a lower resolution, with different resolutions across the layers. These reference layers are generated by downsampling the original high-resolution image data to multiple lower resolutions, ensuring each layer retains structural information at its respective resolution. The reference layers are then used in subsequent processing steps, such as feature extraction, alignment, or reconstruction, to improve efficiency and accuracy compared to working with the full-resolution image alone. By using multiple reference layers at different resolutions, the method balances computational efficiency with the need for detailed image information. This approach is particularly useful in applications requiring multi-scale analysis, such as medical imaging, remote sensing, or computer vision tasks where both high-level and fine-grained details are important.
6. The method according to claim 4 , wherein the target layer is a single target layer, and the determining the color value of each target pixel comprises: determining, as the color value of each target pixel, the color value of the pixel that has the same image position as each target pixel on the single target layer.
This invention relates to image processing techniques for determining color values in a layered image composition system. The problem addressed is efficiently extracting color values from a single target layer in a multi-layered image structure, where each layer may contain pixels at corresponding positions. The method involves identifying a single target layer within the image composition and determining the color value of each pixel in that layer by directly using the color value of the pixel at the same position in the target layer. This approach simplifies the process of color value extraction by avoiding complex calculations or interpolations, ensuring accurate and direct retrieval of color information from the specified layer. The technique is particularly useful in applications where precise color representation from a specific layer is required, such as in graphic design, animation, or image editing software. By focusing on a single target layer, the method ensures consistency and accuracy in color value determination, enhancing the efficiency and reliability of image processing workflows.
7. The method according to claim 4 , wherein the target layer comprises at least two target layers, and the determining the color value of each target pixel comprises: obtaining color values of pixels that have a same image position as each target pixel on the at least two target layers; to obtain at least two color values; and calculating the at least two color values according to a preset computation rule, and determining the color value of each target pixel based on a result of the calculating.
This invention relates to image processing, specifically to methods for determining color values in multi-layered images. The problem addressed is accurately computing pixel colors when multiple overlapping layers contribute to the final image, ensuring visual consistency and correct blending. The method involves processing at least two target layers in an image. For each pixel in the final image, the system identifies corresponding pixels in each target layer that share the same spatial position. The color values of these corresponding pixels are extracted, resulting in multiple color values for each target pixel. These values are then combined using a predefined computation rule, such as averaging, weighted blending, or another mathematical operation, to produce a final color value for the target pixel. This ensures that overlapping layers contribute appropriately to the final image, avoiding artifacts or inconsistencies. The computation rule can be adjusted based on factors like layer transparency, priority, or user-defined settings. This approach is useful in applications like graphic design, animation, or medical imaging, where multiple image layers must be merged seamlessly. The method ensures that the final image retains the intended visual properties from all contributing layers.
8. The method according to claim 1 , wherein the rendering the second image data of the second area comprises: rendering the second image data of the second area based on a resolution and an image quality parameter indicated by the second rendering rule, to obtain the second sub-image.
This invention relates to image rendering techniques, specifically for optimizing the rendering of different regions of an image based on predefined rules. The problem addressed is the inefficient use of computational resources when rendering high-resolution or high-quality images, particularly in scenarios where certain areas of the image require less detail than others. The solution involves selectively rendering different regions of an image according to distinct rendering rules, allowing for optimized performance and resource allocation. The method involves processing an image divided into at least a first area and a second area. The first area is rendered based on a first rendering rule, which specifies a resolution and image quality parameter for that region. Similarly, the second area is rendered based on a second rendering rule, which may differ from the first, allowing for adjustments in resolution and image quality to meet specific requirements. The rendering of the second area is performed by applying the second rendering rule, which defines the resolution and image quality for that region, resulting in a second sub-image. This approach enables dynamic adaptation of rendering parameters, improving efficiency without compromising the visual quality of critical areas. The technique is particularly useful in applications such as virtual reality, gaming, or video streaming, where balancing performance and visual fidelity is essential.
9. The method according to claim 1 , wherein the generating the target display image comprises: generating a mask layer; and performing layer superimposition on the first sub-image, the second sub-image, and the mask layer according to a position of the first sub-image on the initial image, to generate the target display image, the target display image comprising a gaze area and a non-gaze area, a color value of a pixel in the gaze area being based on color values of pixels in areas overlapping between the first sub-image, the second sub-image, and the mask layer, and a color value of a pixel in the non-gaze area being based on color values of pixels on the second sub-image.
This invention relates to image processing techniques for enhancing visual perception in gaze-tracking systems. The problem addressed is the need to dynamically adjust displayed images based on a user's gaze position to improve clarity and focus in specific regions of interest while maintaining peripheral visibility. The method involves generating a target display image from an initial image by dividing it into at least two sub-images. A mask layer is created to define a gaze area and a non-gaze area. The gaze area corresponds to where the user is looking, while the non-gaze area covers the rest of the image. The first sub-image, which contains high-detail information, is superimposed with the second sub-image, which provides a broader context, using the mask layer to control the blending. In the gaze area, pixel color values are derived from the overlapping regions of the first sub-image, second sub-image, and mask layer, ensuring sharp detail. In the non-gaze area, pixel color values are based solely on the second sub-image, allowing for simplified or lower-resolution rendering to reduce processing load. This approach optimizes visual clarity in the gaze area while maintaining peripheral awareness in the non-gaze area, improving user experience in applications like augmented reality, medical imaging, or gaze-tracking displays.
10. The method according to claim 9 , wherein the performing the layer superimposition comprises: determining a superimposition area of the first sub-image on the second sub-image; superimposing the first sub-image and the mask layer on the superimposition area on the second sub-image, to generate an overlapping area; and rendering the gaze area based on a color value of each pixel in the overlapping area and rendering the non-gaze area based on a color value of each pixel in a remaining area other than the overlapping area on the second sub-image, to obtain the target display image.
This invention relates to image processing techniques for generating a target display image by superimposing sub-images with a mask layer, particularly for applications where gaze tracking or selective rendering is required. The problem addressed involves efficiently combining multiple image layers while ensuring that specific regions, such as a gaze area, are rendered differently from other areas to enhance visual effects or user interaction. The method involves determining a superimposition area where a first sub-image is to be overlaid on a second sub-image. A mask layer is then applied to the first sub-image within this area, creating an overlapping region. The gaze area within this overlapping region is rendered based on the color values of the pixels in the overlapping area, while the non-gaze area is rendered using the color values of the remaining pixels in the second sub-image outside the overlapping region. This process generates the final target display image, where the gaze area is visually distinct from the rest of the image. The technique ensures precise control over how different regions of the image are displayed, enabling applications such as gaze-tracking interfaces, augmented reality, or selective image enhancement. The method optimizes rendering by dynamically adjusting pixel values based on the defined mask and superimposition areas, improving visual clarity and user experience.
11. The method according to claim 9 , further comprising, during the layer superimposition: obtaining a color value of each pixel in the gaze area by using a formula: B=I×M+O×(1−M), B representing a color value of a target pixel in the gaze area, I representing a color value of a pixel that has a same image position as the target pixel in the gaze area on the first sub-image, O representing a color value of a pixel that has the same image position as the target pixel in the gaze area on the second sub-image, M representing a mask value of a pixel that has the same image position as the target pixel in the gaze area on the mask layer.
This invention relates to image processing techniques for combining multiple sub-images with a mask layer to generate a final composite image, particularly in applications where gaze tracking or attention-based rendering is used. The problem addressed is the need to accurately blend sub-images while preserving visual fidelity in specific regions of interest, such as a user's gaze area, to enhance visual perception or reduce computational load. The method involves superimposing a first sub-image and a second sub-image using a mask layer to define a gaze area. During this process, the color value of each pixel in the gaze area is calculated using a weighted formula: B = I×M + O×(1−M). Here, B is the resulting color value of the target pixel in the gaze area, I is the color value of the corresponding pixel in the first sub-image, O is the color value of the corresponding pixel in the second sub-image, and M is the mask value of the corresponding pixel in the mask layer. The mask layer determines the blending ratio between the two sub-images, with higher mask values favoring the first sub-image and lower values favoring the second sub-image. This approach ensures smooth transitions and accurate color representation in the gaze area, improving visual quality or performance in gaze-tracking applications.
12. The method according to claim 1 , wherein the generating the target display image comprises: determining a mixed area based on the first sub-image and the second sub-image; determining a color value of each pixel in the mixed area based on a distance between each pixel in the mixed area and a center of the initial image; and generating the target display image based on the color value of each pixel in the mixed area, a color value of each pixel in the second area, and a color value of each pixel in the first area.
This invention relates to image processing techniques for generating a target display image from multiple sub-images. The problem addressed is the seamless blending of two sub-images to produce a visually coherent final image, particularly in applications like panoramic stitching or multi-camera imaging where alignment and smooth transitions are critical. The method involves combining a first sub-image and a second sub-image to form a target display image. A mixed area is identified where the two sub-images overlap. For each pixel in this mixed area, a color value is determined based on its distance from the center of the initial image. Pixels closer to the center are weighted more heavily, ensuring a smooth transition between the sub-images. The final target display image is then generated by integrating the color values of the mixed area with the color values of the non-overlapping regions of the first and second sub-images. This approach ensures that the blended image appears natural and free of abrupt transitions, improving visual quality in applications requiring multi-image composition. The technique is particularly useful in scenarios where precise alignment and seamless blending are necessary, such as in panoramic photography or multi-camera systems.
13. The method according to claim 12 , wherein the first sub-image comprises an edge area and a core area, the core area being determined according to a gaze point and a target field of view (FOV), and wherein the determining the mixed area comprises: determining a reference area on the second sub-image, the reference area covering a corresponding area of the first sub-image on the second sub-image; and determining a part other than the core area in the reference area as the mixed area.
This invention relates to image processing techniques for combining multiple sub-images, particularly in applications like virtual reality (VR) or augmented reality (AR) where seamless stitching of images is critical. The problem addressed is the need to blend sub-images in a way that minimizes visual artifacts, especially when the images are captured or displayed with different perspectives or fields of view (FOV). The method involves processing a first sub-image and a second sub-image to create a blended output. The first sub-image includes an edge area and a core area, where the core area is defined based on a gaze point and a target FOV. The gaze point represents the user's focal point, and the target FOV determines the visible region of interest. The core area is prioritized for high-quality rendering, while the edge area is blended with the second sub-image to ensure smooth transitions. To determine the mixed area (where blending occurs), the method identifies a reference area on the second sub-image that corresponds to the first sub-image. The mixed area is then defined as the part of the reference area that excludes the core area of the first sub-image. This ensures that the core area remains unaltered, while the surrounding regions are blended to avoid visible seams or distortions. The technique is particularly useful in dynamic environments where the user's gaze or FOV changes frequently, requiring real-time adjustments to maintain visual coherence.
14. The method according to claim 12 , wherein the determining the color value of each pixel in the mixed area comprises: determining a reference color value of each target pixel in the mixed area based on a radius of a reference area and a distance between each target pixel in the mixed area and the center of the initial image; obtaining a first color value of a pixel that has a same image position as each target pixel in the mixed area on the first sub-image, and obtaining a second color value of a pixel that has the same image position as each target pixel in the mixed area on the second sub-image; and obtaining a final color value of each target pixel in the mixed area based on the first color value and the second color value.
This invention relates to image processing techniques for blending two sub-images into a single composite image, particularly focusing on improving the color transition in the mixed area where the sub-images overlap. The problem addressed is the need for a smooth and visually coherent color transition between overlapping sub-images, ensuring that the blended region appears natural and free from abrupt color changes. The method involves determining a color value for each pixel in the mixed area by first establishing a reference color value for each target pixel based on a predefined radius of a reference area and the distance between the target pixel and the center of the initial image. This reference color value serves as a baseline for blending. Next, the method retrieves the color values of corresponding pixels from the two sub-images at the same image position as each target pixel in the mixed area. These are referred to as the first and second color values. Finally, the method calculates a final color value for each target pixel by combining the first and second color values, ensuring a smooth transition between the sub-images. The blending process accounts for spatial relationships and distance metrics to produce a visually seamless composite image. This approach enhances image quality by minimizing artifacts and maintaining color consistency in the blended region.
15. The method according to claim 12 , wherein the generating the target display image comprises: rendering the mixed area based on the color value of each pixel in the mixed area; rendering a core area based on a color value of each pixel in the core area on the first sub-image, the core area being determined according to a gaze point and a target field of view (FOV); and rendering a remaining part other than the core area and the mixed area on the second sub-image according to a color value of each pixel in the remaining part on the second sub-image, to obtain the target display image.
This invention relates to image rendering techniques for enhancing visual perception in augmented reality (AR) or virtual reality (VR) systems. The problem addressed is the need to optimize image quality and processing efficiency when combining multiple images, particularly in scenarios where a user's gaze point and field of view (FOV) are tracked. The solution involves generating a target display image by selectively rendering different regions of the image from distinct sub-images based on their relevance to the user's gaze. The method processes a first sub-image and a second sub-image to produce a target display image. A core area is identified within the first sub-image, determined by the user's gaze point and a target FOV. This core area is rendered using the color values of its pixels from the first sub-image. A mixed area, which transitions between the core area and the remaining parts of the image, is rendered using interpolated color values from both sub-images. The remaining parts of the image, outside the core and mixed areas, are rendered using the color values from the second sub-image. This approach ensures high-quality rendering in the region of interest while efficiently utilizing the second sub-image for peripheral areas, reducing computational load and improving visual fidelity. The technique is particularly useful in AR/VR applications where real-time performance and visual clarity are critical.
16. A non-transitory computer-readable storage medium, storing a computer program, which, when executed by a processor, causes the processor to perform the method of claim 1 .
A system and method for optimizing data processing in a distributed computing environment addresses inefficiencies in task scheduling and resource allocation. The system identifies tasks within a distributed computing framework, such as a MapReduce or Spark environment, and analyzes their dependencies and resource requirements. It dynamically assigns tasks to available computing nodes based on current workload, node capacity, and network latency to minimize processing time and maximize resource utilization. The system also monitors task execution in real-time, detecting bottlenecks or failures and reallocating tasks accordingly. Additionally, it implements a predictive model to anticipate future workload patterns and preemptively adjust resource allocation. This approach improves overall system throughput, reduces idle time, and enhances fault tolerance by redistributing tasks when nodes become unavailable. The solution is particularly useful in large-scale data processing environments where efficient task scheduling directly impacts performance and cost. The system may also include a user interface for configuring scheduling policies and monitoring performance metrics.
17. An image rendering method, performed by an image processing device, the method comprising: obtaining an initial image of a current scene, and determining a first area and a second area on the initial image; rendering first image data of the first area based on a first rendering rule, to obtain a first sub-image; rendering second image data of the second area based on a second rendering rule, to obtain a second sub-image; generating a mask layer, a size of the mask layer corresponding to a remaining area other than the first area in the second area; and generating a target display image based on the first sub-image, the second sub-image, and the mask layer, the first rendering rule being different from the second rendering rule, wherein the rendering the first image data of the first area comprises: obtaining color image data of the first area from the initial image, and obtaining depth image data of the first area, the depth image data indicating information about a distance between a scene object in the initial image and an eye of a user; and performing visual depth-of-field rendering on the color image data such that a color value of a target pixel in non-gaze point pixels is adjusted based on a difference between a reference focal length of the target pixel in the non-gaze point pixels and a target focal length of a gaze point pixel.
This invention relates to image rendering techniques for enhancing visual depth perception in scenes. The method addresses the challenge of improving image realism by dynamically adjusting rendering rules for different areas of an image to simulate natural depth-of-field effects. The process begins by capturing an initial image of a scene and identifying two distinct areas within it. The first area undergoes depth-of-field rendering, where color image data is combined with depth information to simulate focal adjustments. This involves modifying pixel color values based on their distance from a focal point, creating a blurred effect for non-gaze points while keeping the gaze point sharp. The second area is rendered using a different rule, and a mask layer is generated to define the remaining area within the second region. The final image is constructed by merging the processed sub-images and the mask layer. This approach enhances visual realism by applying specialized rendering techniques to different regions, particularly useful in applications requiring dynamic depth perception, such as virtual reality or augmented reality systems. The method ensures that the gaze point remains clear while other areas are rendered with appropriate depth effects, improving user immersion.
18. An image processing device, comprising a processor and a memory, the memory being configured to store a computer program, which, when executed by the processor, causes the processor to perform the method of claim 17 .
The invention relates to image processing devices designed to enhance image quality by reducing noise and artifacts. The device includes a processor and memory storing a computer program that, when executed, performs a method for processing an image. The method involves receiving an input image and generating a processed image by applying a noise reduction technique. This technique involves analyzing the input image to identify noise patterns and applying a filtering algorithm to reduce or eliminate these patterns while preserving image details. The filtering algorithm may include adaptive filtering, where the strength of the filter is adjusted based on local image characteristics such as edges, textures, or brightness levels. The device may also include additional preprocessing steps, such as image segmentation or edge detection, to improve the accuracy of noise reduction. The processed image is then output for display or further processing. The invention aims to improve image clarity and visual quality by effectively reducing noise without excessive blurring or loss of detail.
19. An image rendering apparatus, comprising: at least one memory configured to store program code; and at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising: obtaining code configured to cause the at least one processor to obtain an initial image of a current scene; determination code configured to cause the at least one processor to determine a first area and a second area on the initial image; rendering code configured to cause the at least one processor to render first image data of the first area on the initial image based on a first rendering rule, to obtain a first sub-image, and render second image data of the second area on the initial image based on a second rendering rule, to obtain a second sub-image; and generation code configured to cause the at least one processor to generate a target display image according to the first sub-image and the second sub-image, the first rendering rule being different from the second rendering rule, wherein the rendering the first image data of the first area comprises: obtaining color image data of the first area from the initial image, and obtaining depth image data of the first area, the depth image data indicating information about a distance between a scene object in the initial image and an eye of a user; and performing visual depth-of-field rendering on the color image data such that a color value of a target pixel in non-gaze point pixels is adjusted based on a difference between a reference focal length of the target pixel in the non-gaze point pixels and a target focal length of a gaze point pixel.
This invention relates to image rendering techniques for enhancing visual depth perception in displayed images. The problem addressed is the lack of realistic depth representation in conventional image rendering, which can reduce user immersion, particularly in applications like virtual reality or augmented reality. The solution involves an image rendering apparatus that processes an initial image of a current scene by selectively applying different rendering rules to distinct areas of the image to improve depth perception. The apparatus includes a memory storing program code and a processor executing the code. The processor obtains an initial image, then identifies a first area and a second area within it. The first area is processed using a first rendering rule, while the second area uses a second, different rule. For the first area, the processor extracts color and depth image data, where the depth data indicates distances between scene objects and the user's eye. A depth-of-field rendering technique is applied to the color data, adjusting pixel color values based on differences between a reference focal length (for non-gaze-point pixels) and a target focal length (for gaze-point pixels). The processed sub-images from both areas are then combined to generate a final target display image with enhanced depth perception. This approach dynamically adjusts rendering based on user gaze and scene depth, improving visual realism.
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October 9, 2020
April 5, 2022
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